MR Center of Excellence, Department of Radiology, Medical University of Vienna, Vienna, Austria.
NMR Biomed. 2013 Dec;26(12):1796-805. doi: 10.1002/nbm.3019. Epub 2013 Sep 4.
The goal of this study was to evaluate a new method of combining multi-channel (1)H MRSI data by direct use of a matching imaging scan as a reference, rather than computing sensitivity maps. Seven healthy volunteers were measured on a 7-T MR scanner using a head coil with a 32-channel array coil for receive-only and a volume coil for receive/transmit. The accuracy of prediction of the phase of the (1)H MRSI data with a fast imaging pre-scan was investigated with the volume coil. The array coil (1)H MRSI data were combined using matching imaging data as coil combination weights. The signal-to-noise ratio (SNR), spectral quality, metabolic map quality and Cramér-Rao lower bounds were then compared with the data obtained by two standard methods, i.e. using sensitivity maps and the first free induction decay (FID) data point. Additional noise decorrelation was performed to further optimize the SNR gain. The new combination method improved significantly the SNR (+29%), overall spectral quality and visual appearance of metabolic maps, and lowered the Cramér-Rao lower bounds (-34%), compared with the combination method based on the first FID data point. The results were similar to those obtained by the combination method using sensitivity maps, but the new method increased the SNR slightly (+1.7%), decreased the algorithm complexity, required no reference coil and pre-phased all spectra correctly prior to spectral processing. Noise decorrelation further increased the SNR by 13%. The proposed method is a fast, robust and simple way to improve the coil combination in (1)H MRSI of the human brain at 7 T, and could be extended to other (1)H MRSI techniques.
本研究的目的是评估一种新的方法，通过直接使用匹配的成像扫描作为参考，而不是计算灵敏度图，来组合多通道（1）H MRSI 数据。7 名健康志愿者在 7TMR 扫描仪上进行测量，使用头部线圈进行接收，32 通道阵列线圈进行仅接收，容积线圈进行接收/传输。使用容积线圈研究了快速成像预扫描预测（1）H MRSI 数据相位的准确性。使用匹配的成像数据作为线圈组合权重来组合阵列线圈（1）H MRSI 数据。然后将信号噪声比（SNR）、光谱质量、代谢图质量和克拉美罗下界与两种标准方法获得的数据进行比较，即使用灵敏度图和第一个自由感应衰减（FID）数据点。进行额外的噪声去相关以进一步优化 SNR 增益。与基于第一个 FID 数据点的组合方法相比，新的组合方法显著提高了 SNR（+29%）、整体光谱质量和代谢图的视觉外观，并且降低了克拉美罗下界（-34%）。结果与使用灵敏度图的组合方法相似，但新方法略微提高了 SNR（+1.7%），降低了算法复杂度，不需要参考线圈，并在光谱处理之前正确预相所有光谱。噪声去相关进一步将 SNR 提高了 13%。该方法是一种快速、稳健且简单的方法，可以在 7T 下提高人脑（1）H MRSI 的线圈组合，并且可以扩展到其他（1）H MRSI 技术。
